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Package as Skill

An R package that documents its functions properly is already what MCP is trying to wire together: tools (functions), descriptions (.Rd files), and an invocation mechanism (library()). corteza walks the .Rd tree at session start and turns the exports into JSON-Schema tool definitions — same shape the Anthropic / OpenAI / Moonshot APIs expect. No server, no schema by hand, no protocol.

This vignette walks the setup with fortunes, quotes from the R-help archives. It returns a structured S3 object, and the demo is more fun than configuring a JSON parser.

Setup

Two steps. Total wiring: one config line.

1. Install the package

install.packages("fortunes")

2. Register it as a skill

Add a skill_packages entry to corteza’s config. Either project-local at <cwd>/.corteza/config.json or global at tools::R_user_dir("corteza", "config")/config.json:

{
  "skill_packages": ["fortunes"]
}

That’s it. The string form registers every export. For larger packages, the object form picks specific functions:

{
  "skill_packages": [
    {"package": "fortunes", "functions": ["fortune"]}
  ]
}

Use

corteza::chat()

The startup banner shows the tool count climbing — corteza::chat() reports 30 tools instead of 29. Then ask the agent to use it:

> Find a fortune by Brian Ripley about types or coercion.

You’ll see the progress hint fire as the agent invokes the tool:

  [fortunes::fortune] author=Brian Ripley (8 lines)

The fortune object comes back with quote, author, context, source, and date fields, and the agent paraphrases or quotes from there. Try variations:

• “Pick a random fortune and tell me which R personality you’d most love to argue with about it.”
• “Use fortune(showMatches = TRUE) to find anything about NULL, then summarize the consensus.”
• “Show me three fortunes about S4 classes and rank them by snark.”

What makes a package a good fit

The R community has 20,000 CRAN packages but not all of them work cleanly as agent skills. The shape that fits:

• Returns structured data (data.frames, lists, S3 objects). The LLM consumes the result programmatically.
• No printing to stderr on the happy path. Anything that cat()s status updates or paints with crayon:: pollutes the tool result.
• No oops() or stop() for non-error cases. If the function calls stop() because you’re not in the right directory, it’s designed for a human at the console, not a tool harness.
• .Rd parameters that match formals(). CRAN already enforces this with R CMD check, so any current CRAN package qualifies.

Counter-example: gitr wraps system2("git", ...) cleanly enough but cat()s colored output and calls oops() when not in a git repo. Wrapping it as a skill needed utils::capture.output() and grew the code instead of shrinking it. Built for humans, not for plumbing.

The test: would you call this function from another function and trust the return value? If yes, it’s a candidate.

Why no MCP server

A live MCP server ships every tool’s JSON schema into the system prompt at connect time. Twenty tools at ~400 tokens each is 8,000 tokens of startup overhead before the agent has done anything. The CLI / package-as-skill path pays roughly zero startup tax (corteza already has bash, run_r, read_file baked in), and lazy lookup via saber::pkg_help() costs ~200 tokens for a tool the agent actually needs.

Same tool surface, different cost curve. corteza’s MCP server (corteza::serve()) still exists for clients that need it (Claude Code, Codex, etc), but it’s no longer the only path — the same skill registry feeds both.

Where to go next

• The derivation pipeline is summarized in the package vignette and package source; the earlier external blog post is no longer linked because its URL no longer resolves reliably.
• Discover what’s in a package the same way the agent does: Rscript --vanilla -e 'saber::pkg_exports("fortunes")' and Rscript --vanilla -e 'saber::pkg_help("fortune", "fortunes")'.
• For your own functions, drop an .R file in <project>/.corteza/skills/ calling register_skill_from_fn("name", my_fn). Loaded on every session start, no installation required.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.